v 2.2.2

Update README

README.md

@@ -1,23 +1,27 @@
-# XYPlot: Comfy plugin
+# ImagesGrid: Comfy plugin
 
 
-![Image](./workflows/xy_plot_mini.png)
-[Workflows](./workflows/xy_plot_mini.json)
-![Image](./workflows/xy_plot_base.png)
-[Workflows](./workflows/xy_plot_base.json)
+## Preview
+
+### Simple grid of images
+
+![Image](https://github.com/LEv145/images-grid-comfy-plugin/blob/main/workflows/mini.png?raw=true)
+
+### XYZPlot, like in auto1111, but with more settings
+
+![Image](https://github.com/LEv145/images-grid-comfy-plugin/blob/main/workflows/base.png?raw=true)
+
+Workflows: https://github.com/LEv145/images-grid-comfy-plugin/tree/main/workflows
 
 
 ## How to use
 
-### Install
+1. Download the latest stable release:
+https://github.com/LEv145/images-grid-comfy-plugin/archive/refs/heads/main.zip
 
-```
-cd custom_nodes  # From comfy path
-git clone https://github.com/LEv145/XY-plot-comfy-plugin XYPlot
-```
-### Update
+2. Unpack the node to `custom_nodes`, for example in a folder `custom_nodes/ImagesGrid/`
 
-```
-cd custom_nodes/XYPlot
-git pull
-```
+
+## Source
+
+https://github.com/LEv145/images-grid-comfy-plugin

__init__.py

@@ -1,7 +1,16 @@
-from .src import LatentCombineNode, XYPlotNode
+from .src import (
+    LatentCombineNode,
+    ImagesGridByColumnsNode,
+    ImagesGridByRowsNode,
+    ImageCombineNode,
+    GridAnnotationNode,
+)
 
 
 NODE_CLASS_MAPPINGS = {
-    "XYPlot": XYPlotNode,
     "LatentCombine": LatentCombineNode,
+    "ImagesGridByColumns": ImagesGridByColumnsNode,
+    "ImagesGridByRows": ImagesGridByRowsNode,
+    "ImageCombine": ImageCombineNode,
+    "GridAnnotation": GridAnnotationNode,
 }

src/__init__.py

@@ -1,2 +1,4 @@
-from .nodes.xy_plot import XYPlotNode
+from .nodes.images_grid import ImagesGridByColumnsNode, ImagesGridByRowsNode
 from .nodes.latent_combine import LatentCombineNode
+from .nodes.image_combine import ImageCombineNode
+from .nodes.grid_annotation import GridAnnotationNode

src/base.py

@@ -1,16 +1,10 @@
 import typing as t
-from dataclasses import dataclass
+from pathlib import Path
 
 
-class BasePlotNode():
-    CATEGORY: str = "XYPlot"
+STATIC_PATH = Path(__file__).parent.parent / "static"
+
+
+class BaseNode():
+    CATEGORY: str = "ImagesGrid"
     FUNCTION: str = "execute"
-
-
-@dataclass
-class KSamplerXYPlotInput():
-    setting: str
-    value: int
-
-
-Image = t.Any

src/nodes/float_image_combine.py

@@ -1,23 +0,0 @@
-import typing as t
-
-from ..base import BasePlotNode, Image
-
-
-class FloatImageCombineNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGES",)
-
-    @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
-        return {
-            "required": {
-                "float_image_1": ("IMAGES",),
-                "float_image_2": ("IMAGES",),
-            },
-        }
-
-    def execute(
-        self,
-        float_image_1: t.List[Image],
-        float_image_2: t.List[Image],
-    ) -> t.Tuple[t.List[Image]]:
-        return (float_image_1 + float_image_2,)

src/nodes/grid_annotation.py

@@ -0,0 +1,49 @@
+import typing as t
+
+from PIL import ImageFont
+
+from ..base import BaseNode, STATIC_PATH
+from ..utils import Annotation
+
+
+class GridAnnotationNode(BaseNode):
+    RETURN_TYPES: tuple[str] = ("GRID_ANNOTATION",)
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
+        return {
+            "required": {
+                "column_texts": ("STRING", {"multiline": False}),
+                "row_texts": ("STRING", {"multiline": False}),
+                "font_size": ("INT", {"default": 50, "min": 1}),
+            },
+        }
+
+    def execute(
+        self,
+        column_texts: str,
+        row_texts: str,
+        font_size: int,
+    ) -> tuple[Annotation]:
+        font = ImageFont.truetype(str(STATIC_PATH / "Roboto-Regular.ttf"), size=font_size)
+        column_texts_list = self._set_value_to_texts_list(
+            self._get_texts_from_string(column_texts),
+        )
+        row_texts_list = self._set_value_to_texts_list(
+            self._get_texts_from_string(row_texts),
+        )
+
+        result = Annotation(column_texts=column_texts_list, row_texts=row_texts_list, font=font)
+        return (result,)
+
+    def _get_texts_from_string(self, string: str) -> list[str]:
+        return [
+            result
+            for i in string.split(";")
+            if (result := i.strip()) != ""
+        ]
+
+    def _set_value_to_texts_list(self, texts_list: list[str]) -> list[str]:
+        if not texts_list:
+            return ["None"]
+        return texts_list

src/nodes/image_combine.py

@@ -0,0 +1,27 @@
+import typing as t
+
+import torch
+
+from ..base import BaseNode
+
+
+class ImageCombineNode(BaseNode):
+    RETURN_TYPES: tuple[str] = ("IMAGE",)
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
+        return {
+            "required": {
+                "image_1": ("IMAGE",),
+                "image_2": ("IMAGE",),
+            },
+        }
+
+    def execute(
+        self,
+        image_1: torch.Tensor,
+        image_2: torch.Tensor,
+    ) -> tuple[torch.Tensor]:
+        result = torch.cat((image_1, image_2), 0)
+
+        return (result,)

src/nodes/image_set_area.py

@@ -1,18 +0,0 @@
-import typing as t
-
-from ..base import BasePlotNode, Image
-
-
-class ImageSetAreaNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGES",)
-
-    @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
-        return {
-            "required": {
-                "image": ("IMAGE",),
-            },
-        }
-
-    def execute(self, image: Image) -> t.Tuple[t.List[Image]]:
-        return ([image],)

src/nodes/images_grid.py

@@ -0,0 +1,66 @@
+import typing as t
+
+import torch
+
+from ..base import BaseNode
+from ..utils import (
+    tensor_to_pillow,
+    pillow_to_tensor,
+    create_images_grid_by_columns,
+    create_images_grid_by_rows,
+    Annotation,
+)
+
+class BaseImagesGridNode(BaseNode):
+    RETURN_TYPES: tuple[str] = ("IMAGE",)
+
+    @classmethod
+    def _create_input_types(cls, coordinate_name: str) -> dict[str, t.Any]:
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "gap": ("INT", {"default": 0, "min": 0}),
+                coordinate_name: ("INT", {"default": 1, "min": 1}),
+            },
+            "optional": {
+                "annotation": ("GRID_ANNOTATION",),
+            }
+        }
+
+    def _create_execute(
+        self,
+        function: t.Callable,
+        \
+        images: torch.Tensor,
+        gap: int,
+        annotation: Annotation | None = None,
+        **kw,
+    ) -> tuple[torch.Tensor]:
+        pillow_images = [tensor_to_pillow(i) for i in images]
+        pillow_grid = function(
+            images=pillow_images,
+            gap=gap,
+            annotation=annotation,
+            **kw,
+        )
+        tensor_grid = pillow_to_tensor(pillow_grid)
+
+        return (tensor_grid,)
+
+
+class ImagesGridByColumnsNode(BaseImagesGridNode):
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
+        return cls._create_input_types("max_columns")
+
+    def execute(self, **kw) -> tuple[torch.Tensor]:
+        return self._create_execute(create_images_grid_by_columns, **kw)
+
+
+class ImagesGridByRowsNode(BaseImagesGridNode):
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
+        return cls._create_input_types("max_rows")
+
+    def execute(self, **kw) -> tuple[torch.Tensor]:
+        return self._create_execute(create_images_grid_by_rows, **kw)

src/nodes/k_sampler_xy_plot.py

@@ -1,62 +0,0 @@
-import typing as t
-
-from nodes import KSamplerAdvanced  # type: ignore
-
-from ..base import BasePlotNode, Image, KSamplerXYPlotInput
-
-
-class KSamplerXYPlotNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGES",)
-
-    def __init__(self) -> None:
-        self._sampler = KSamplerAdvanced()
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        result = KSamplerAdvanced.INPUT_TYPES()
-        result["required"]["vae"] = ("VAE", )
-        #result["required"]["x_items"] = ("XYPlotItem",)
-        #result["required"]["y_items"] = ("XYPlotItem",)
-        return result
-
-    def execute(
-        self,
-        vae,
-        #x_items,
-        #y_items,
-        **sampler_kw,
-    ) -> tuple[t.List[Image]]:
-        x_items = [
-            KSamplerXYPlotInput(value=1, setting="cfg"),
-            KSamplerXYPlotInput(value=2, setting="cfg"),
-        ]
-        y_items = [
-            KSamplerXYPlotInput(value=1, setting="noise_seed"),
-            KSamplerXYPlotInput(value=2, setting="noise_seed"),
-        ]
-
-        latents = self._sample_latents(
-            x_items=x_items,
-            y_items=y_items,
-            sampler_kw=sampler_kw,
-        )
-        result = list(self._decode_latents(latents=latents, vae=vae))
-        print(result)
-        print(type(result[0]))
-
-        return (result,)
-
-    def _sample_latents(self, x_items, y_items, sampler_kw):
-        for x in x_items:
-            for y in y_items:
-                sampler_settings = sampler_kw.copy()
-                sampler_settings[x.setting] = x.value
-                sampler_settings[y.setting] = y.value
-
-                yield self._sampler.sample(**sampler_settings)[0]
-
-    def _decode_latents(self, latents, vae) -> t.Iterable[Image]:
-        return (
-            vae.decode(i["samples"])
-            for i in latents
-        )

src/nodes/latent_combine.py

@@ -2,14 +2,14 @@ import typing as t
 
 import torch
 
-from ..base import BasePlotNode, Image
+from ..base import BaseNode
 
 
-class LatentCombineNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("LATENT",)
+class LatentCombineNode(BaseNode):
+    RETURN_TYPES: tuple[str] = ("LATENT",)
 
     @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
+    def INPUT_TYPES(cls) -> dict[str, t.Any]:
         return {
             "required": {
                 "latent_1": ("LATENT",),
@@ -19,11 +19,9 @@ class LatentCombineNode(BasePlotNode):
 
     def execute(
         self,
-        latent_1: t.Dict[str, t.Any],
-        latent_2: t.Dict[str, t.Any],
-    ) -> t.Tuple[t.Dict[str, t.Any]]:
-        latent_1_samples = latent_1["samples"]
-        latent_2_samples = latent_2["samples"]
-        samples = torch.cat((latent_1_samples, latent_2_samples), 0)
+        latent_1: dict[str, torch.Tensor],
+        latent_2: dict[str, torch.Tensor],
+    ) -> tuple[dict[str, torch.Tensor]]:
+        samples = torch.cat((latent_1["samples"], latent_2["samples"]), 0)
 
         return ({"samples": samples},)

src/nodes/xy_plot.py

@@ -1,30 +0,0 @@
-import typing as t
-
-from ..base import BasePlotNode, Image
-from ..utils import tensor_to_pillow, pillow_to_tensor, create_image_grid
-
-
-class XYPlotNode(BasePlotNode):
-    RETURN_TYPES: t.Tuple[str] = ("IMAGE",)
-
-    @classmethod
-    def INPUT_TYPES(cls) -> t.Dict[str, t.Any]:
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "gap": ("INT", {"default": 0, "min": 0}),
-                "nrow": ("INT", {"default": 1, "min": 1}),
-            },
-        }
-
-    def execute(
-        self,
-        images: Image,
-        nrow: int,
-        gap: int
-    ) -> tuple[Image]:
-        pillow_images = [tensor_to_pillow(i) for i in images]
-        pillow_grid = create_image_grid(pillow_images, nrow=nrow, gap=gap)
-        tensor_grid = pillow_to_tensor(pillow_grid)
-
-        return (tensor_grid,)

src/utils.py

@@ -1,39 +0,0 @@
-import typing as t
-
-import torch
-import numpy as np
-from PIL import Image
-
-
-def tensor_to_pillow(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-
-def pillow_to_tensor(image):
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
-
-def create_image_grid(images: t.List[Image.Image], gap: int, ncol: int):
-    # Calculate the number of rows needed based on the number of images and columns
-    nrow = (len(images) + ncol - 1) // ncol
-
-    # Get the size of the first image to use as a template for the grid
-    size = images[0].size
-
-    # Calculate the total size of the grid with gaps
-    width = size[0] * ncol + gap * (ncol - 1)
-    height = size[1] * nrow + gap * (nrow - 1)
-
-    # Create a new image for the grid
-    grid_image = Image.new("RGB", (width, height), color="white")
-
-    # Iterate over each image and paste it into the grid
-    for i, image in enumerate(images):
-        # Calculate the position of the image in the grid
-        x = (i % ncol) * (size[0] + gap)
-        y = (i // ncol) * (size[1] + gap)
-
-        # Paste the image into the grid
-        grid_image.paste(image, (x, y))
-
-    return grid_image

src/utils/__init__.py

@@ -0,0 +1,6 @@
+from .images_grid import (
+    create_images_grid_by_columns,
+    create_images_grid_by_rows,
+    Annotation,
+)
+from .tensor_convert import tensor_to_pillow, pillow_to_tensor

src/utils/images_grid.py

@@ -0,0 +1,195 @@
+import typing as t
+from dataclasses import dataclass
+from contextlib import suppress
+
+from PIL import Image, ImageDraw, ImageFont
+
+
+@dataclass
+class Annotation():
+    column_texts: list[str]
+    row_texts: list[str]
+    font: ImageFont.FreeTypeFont
+
+
+@dataclass
+class _GridInfo():
+    image: Image.Image
+    gap: int
+    one_image_size: tuple[int, int]
+
+
+def create_images_grid_by_columns(
+    images: list[Image.Image],
+    gap: int,
+    max_columns: int,
+    annotation: Annotation | None = None,
+) -> Image.Image:
+    max_rows = (len(images) + max_columns - 1) // max_columns
+    return _create_images_grid(images, gap, max_columns, max_rows, annotation)
+
+
+def create_images_grid_by_rows(
+    images: list[Image.Image],
+    gap: int,
+    max_rows: int,
+    annotation: Annotation | None = None,
+) -> Image.Image:
+    max_columns = (len(images) + max_rows - 1) // max_rows
+    return _create_images_grid(images, gap, max_columns, max_rows, annotation)
+
+
+def _create_images_grid(
+    images: list[Image.Image],
+    gap: int,
+    max_columns: int,
+    max_rows: int,
+    annotation: Annotation | None,
+) -> Image.Image:
+    size = images[0].size
+    grid_width = size[0] * max_columns + (max_columns - 1) * gap
+    grid_height = size[1] * max_rows + (max_rows - 1) * gap
+
+    grid_image = Image.new("RGB", (grid_width, grid_height), color="white")
+
+    _arrange_images_on_grid(grid_image, images=images, size=size, max_columns=max_columns, gap=gap)
+
+    if annotation is None:
+        return grid_image
+    return _create_grid_annotations(
+        grid_info=_GridInfo(
+            image=grid_image,
+            gap=gap,
+            one_image_size=size,
+        ),
+        column_texts=annotation.column_texts,
+        row_texts=annotation.row_texts,
+        font=annotation.font,
+    )
+
+
+def _arrange_images_on_grid(
+    grid_image: Image.Image,
+    /,
+    images: list[Image.Image],
+    size: tuple[int, int],
+    max_columns: int,
+    gap: int,
+):
+    for i, image in enumerate(images):
+        if image.size != size:
+            image = image.crop((0, 0, *size))
+        x = (i % max_columns) * (size[0] + gap)
+        y = (i // max_columns) * (size[1] + gap)
+
+        grid_image.paste(image, (x, y))
+
+
+def _create_grid_annotations(
+    grid_info: _GridInfo,
+    column_texts,
+    row_texts,
+    font: ImageFont.FreeTypeFont,
+) -> Image.Image:
+    if not column_texts or not row_texts:
+        raise ValueError("Column text or row text is empty")
+
+    grid = grid_info.image
+    margin = font.size // 2
+    left_padding = int(max(map(font.getlength, row_texts))) + 2*margin
+    top_padding = font.size + 2*margin
+
+    image = Image.new(
+        "RGB",
+        (grid.size[0] + left_padding, grid.size[1] + top_padding),
+        color="white",
+    )
+    draw = ImageDraw.Draw(image)
+    draw.font = font  # type: ignore
+
+    _paste_image_to_lower_left_corner(image, grid)
+    _draw_column_text(
+        draw=draw,
+        texts=column_texts,
+        grid_info=grid_info,
+        left_padding=left_padding,
+        top_padding=top_padding,
+    )
+    _draw_row_text(
+        draw=draw,
+        texts=row_texts,
+        grid_info=grid_info,
+        left_padding=left_padding,
+        top_padding=top_padding,
+    )
+
+    return image
+
+
+def _draw_column_text(
+    draw: ImageDraw.ImageDraw,
+    texts: list[str],
+    grid_info: _GridInfo,
+    left_padding: int,
+    top_padding: int,
+) -> None:
+    i = 0
+    x0 = left_padding
+    y0 = 0
+    x1 = left_padding + grid_info.one_image_size[0]
+    y1 = top_padding
+    while x0 != grid_info.image.size[0] + left_padding + grid_info.gap:
+        i  = _draw_text_by_xy((x0, y0, x1, y1), i, draw=draw, texts=texts)
+        x0 += grid_info.one_image_size[0] + grid_info.gap
+        x1 += grid_info.one_image_size[0] + grid_info.gap
+
+
+def _draw_row_text(
+    draw: ImageDraw.ImageDraw,
+    texts: list[str],
+    grid_info: _GridInfo,
+    left_padding: int,
+    top_padding: int,
+) -> None:
+    i = 0
+    x0 = 0
+    y0 = top_padding
+    x1 = left_padding
+    y1 = top_padding + grid_info.one_image_size[1]
+    while y0 != grid_info.image.size[1] + top_padding + grid_info.gap:
+        i  = _draw_text_by_xy((x0, y0, x1, y1), i, draw=draw, texts=texts)
+        y0 += grid_info.one_image_size[1] + grid_info.gap
+        y1 += grid_info.one_image_size[1] + grid_info.gap
+
+
+def _draw_text_by_xy(
+    xy: tuple[int, int, int, int],
+    index: int,
+    \
+    draw: ImageDraw.ImageDraw,
+    texts: list[str],
+) -> int:
+    with suppress(IndexError):
+        _draw_center_text(draw, xy, texts[index])
+    return index + 1
+
+
+def _draw_center_text(
+    draw: ImageDraw.ImageDraw,
+    xy: tuple[int, int, int, int],
+    text: str,
+    fill: t.Any = "black",
+) -> None:
+    _, _, *text_size = draw.textbbox((0, 0), text)
+    draw.text(
+        (
+            (xy[2] - text_size[0] + xy[0]) / 2,
+            (xy[3] - text_size[1] + xy[1]) / 2,
+        ),
+        text,
+        fill=fill,
+    )
+
+
+def _paste_image_to_lower_left_corner(base: Image.Image, image: Image.Image) -> None:
+    base.paste(image, (base.size[0] - image.size[0], base.size[1] - image.size[1]))

src/utils/tensor_convert.py

@@ -0,0 +1,13 @@
+import typing as t
+
+import torch
+import numpy as np
+from PIL import Image
+
+
+def tensor_to_pillow(image: t.Any) -> Image.Image:
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def pillow_to_tensor(image: Image.Image) -> t.Any:
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)

workflows/mini.json

@@ -0,0 +1,799 @@
+{
+  "last_node_id": 74,
+  "last_link_id": 126,
+  "nodes": [
+    {
+      "id": 68,
+      "type": "LoadImage",
+      "pos": [
+        -30,
+        70
+      ],
+      "size": {
+        "0": 315,
+        "1": 102
+      },
+      "flags": {
+        "collapsed": true
+      },
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            119
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "394102.png",
+        "image"
+      ]
+    },
+    {
+      "id": 41,
+      "type": "LoadImage",
+      "pos": [
+        -30,
+        210
+      ],
+      "size": {
+        "0": 315,
+        "1": 102
+      },
+      "flags": {
+        "collapsed": true
+      },
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            120
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
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+}

workflows/xy_plot_mini.json

@@ -1,445 +0,0 @@
-{
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